Abstract

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.

Date

2015

Authors

Michael Pust, Ulf Hermjakob, Kevin Knight, Daniel Marcu, Jonathan May

Conference

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Pages

1143-1154